2026-09-19T17:23:14Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/697982022-01-13T07:54:33Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Randolph E. Kirchain and Joel. P. Clark.
Patanavanich, Siamrut
Massachusetts Institute of Technology. Dept. of Materials Science and Engineering.
Massachusetts Institute of Technology. Department of Materials Science and Engineering
2012-03-16T16:04:05Z
2012-03-16T16:04:05Z
2011
2011
http://hdl.handle.net/1721.1/69798
777955507
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 55-58).
Life cycle assessment (LCA) has gained much interest in the field of product development and decision making. The resource intensiveness of conducting an LCA has slowed more widespread adoption of the methodology. Although some streamlined LCA methodology exists and are currently be applied, there can be a lot of known and unknown uncertainties in the resulting analysis. These uncertainties could sometimes render the LCA results useless for any decision making activities. Thus this thesis proposes the evaluation of probabilistic underspecification in streamlining LCA and estimating a product's life cycle impact to both reduce LCA efforts and increase certainty in the results. This thesis focuses the development and application of probabilistic underspecification in estimating the materials impact of a product. In order to account for the uncertain with the degree of underspecificity, we propose structuring of a classification system that will help associate materials specificity, uncertainty in the materials impact, and the degree of effort to retrieve that information. This will serve as the bases for probabilistic methodology to determine what part of product is important to characterize and invest effort in order to reduce uncertainty in the LCA results with less effort than traditional LCA. Mass can be a key indicator of impact. Therefore, several case studies were conducted comparing the viability of probabilistic underspecification for calculating materials impact value for these products of varied mass compositional characteristics or the degree of mass uniformity. The compositional uniformity was measured by adapting the Herfindahl index used in economics but applied to component-mass share. Despite the difference in the mass uniformity, the methodology significantly and consistently reduced the number of components that needed to be well specified, while retaining a relatively high confidence in the resulting estimates. Probabilistic underspecification shows promise in both reducing LCA efforts and increasing the significance in the material impact assessment of the case studies in this thesis. This process also allows the leveraging of uncertainty and probability to reduce the effort and may help improve the rate at which life cycle assessment may be conducted. With faster LCA, the move towards a sustainable and environmentally responsible growth economy may be sooner realized.
by Siamrut Patanavanich.
S.M.
94 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Materials Science and Engineering.
Exploring the viability of probabilistic underspecification as a viable streamlining method for LCA
Thesis
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<Abstract>Life cycle assessment (LCA) has gained much interest in the field of product development and decision making. The resource intensiveness of conducting an LCA has slowed more widespread adoption of the methodology. Although some streamlined LCA methodology exists and are currently be applied, there can be a lot of known and unknown uncertainties in the resulting analysis. These uncertainties could sometimes render the LCA results useless for any decision making activities. Thus this thesis proposes the evaluation of probabilistic underspecification in streamlining LCA and estimating a product's life cycle impact to both reduce LCA efforts and increase certainty in the results. This thesis focuses the development and application of probabilistic underspecification in estimating the materials impact of a product. In order to account for the uncertain with the degree of underspecificity, we propose structuring of a classification system that will help associate materials specificity, uncertainty in the materials impact, and the degree of effort to retrieve that information. This will serve as the bases for probabilistic methodology to determine what part of product is important to characterize and invest effort in order to reduce uncertainty in the LCA results with less effort than traditional LCA. Mass can be a key indicator of impact. Therefore, several case studies were conducted comparing the viability of probabilistic underspecification for calculating materials impact value for these products of varied mass compositional characteristics or the degree of mass uniformity. The compositional uniformity was measured by adapting the Herfindahl index used in economics but applied to component-mass share. Despite the difference in the mass uniformity, the methodology significantly and consistently reduced the number of components that needed to be well specified, while retaining a relatively high confidence in the resulting estimates. Probabilistic underspecification shows promise in both reducing LCA efforts and increasing the significance in the material impact assessment of the case studies in this thesis. This process also allows the leveraging of uncertainty and probability to reduce the effort and may help improve the rate at which life cycle assessment may be conducted. With faster LCA, the move towards a sustainable and environmentally responsible growth economy may be sooner realized.</Abstract>
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